Hemoglobin A1C Variability as a Risk Factor for Diabetes Complications

持续性高血糖可预测微血管并发症（如视网膜病变和 糖尿病患者的肾脏疾病。然而，血糖控制与器官损伤之间的关系 相当复杂.降低血红蛋白A1c（A1c）可预防或延迟微血管并发症， 心血管疾病（CVD）和死亡率的影响并不一致。因此，需要开发新的 质量措施超越A1c单独，以更好地识别和治疗有并发症和死亡风险的患者。 A1c变异性，如A1c随时间的波动所测量的，是一个强有力的候选者。一些研究表明， A1c变异性增加与微血管疾病和CVD之间存在显著相关性。A1c 变异性携带重要的风险信息，方差度量（如标准差）可能不 临床直觉。因此，我们的目标是开发一种新的A1c变异性质量指标-A1c范围内时间 (TIR)- 帮助临床医生和患者以平衡长期利益和风险的方式控制A1c。我们 将A1c TIR定义为患者A1c水平处于特定目标范围内的天数百分比，基于 其临床特征和临床实践指南。我们将研究A1c TIR在一个概括的 来自退伍军人事务部和Kaiser Permanente的365，000多名患者的全国样本。 我们将应用先进的统计方法，通过使用仪器，严格控制选择偏差。 变量设计，包括过程质量控制。这些方法使我们能够得出因果推论之间 TIR和新发糖尿病并发症的风险。我们将研究A1c TIR的预测因素，我们假设这将 受提供者实践模式、个体患者水平特征和药物的影响。我们将测试 假设A1c TIR越高，糖尿病并发症的风险越低。我们亦会研究 相反-A1c时间超出范围（TOR），对高于（TOR [高]）和低于 范围（TOR [低]），以确定是否与微血管或大血管并发症唯一相关。 然后，我们将研究临床因素，低血糖事件和A1c的快速下降，作为介导因素， TIR与不良结局之间的关系。每一种都与死亡率和糖尿病的早期恶化有关 并发症，分别。这项研究将通过开发一种新的质量测量方法来促进糖尿病护理， 以风险分层的方式识别患者，并帮助临床医生根据患者的 独特的护理目标。这种新的措施将被临床医生在护理点和医疗保健使用 人口健康管理体系。

Persistent hyperglycemia predicts the development of microvascular complications (e.g. retinopathy and nephropathy) in patients with diabetes. However, the relationship between glucose control and organ damage is complex. Reducing hemoglobin A1c (A1c) prevents or delays microvascular complications but cardiovascular disease (CVD) and mortality are inconsistently affected. Thus, there is a need to develop new quality measures beyond A1c alone to better identify and treat patients at risk for complications and mortality. A1c variability, as measured by fluctuations in A1c over time, is a strong candidate. Several studies show a significant relationship between increased A1c variability and microvascular disease and CVD. While A1c variability carries important risk information, variance measures such as standard deviation, may not be clinically intuitive. Thus, our goal is to develop a new quality measure of A1c variability – A1c time in range (TIR) – that helps clinicians and patients control A1c in a way that balances long-term benefits and risks. We will define A1c TIR as the percentage of days a patient's A1c levels are in a specific target range, based on their clinical characteristics and clinical practice guidelines. We will study A1c TIR in a generalizable nationwide sample of over 365,000 patients from the Department of Veterans Affairs and Kaiser Permanente. We will apply advanced statistical methods that stringently control for selection bias by using an instrumental variable design, including process quality controls. These methods allow us to draw causal inferences between TIR and risk of new diabetes complications. We will study the predictors of A1c TIR, which we hypothesize will be affected by provider practice patterns, individual patient-level characteristics and medications. We will test the hypothesis that higher A1c TIR confers lower risk of diabetes complications. We will also study the converse – A1c time out-of-range (TOR), with interest in deviations both above (TOR [high]) and below the range (TOR [low]) to determine if either is uniquely associated with micro- or macrovascular complications. Then we will investigate clinical factors, hypoglycemic events and rapid declines in A1c, as mediators of the relationship between TIR and adverse outcomes. Each is linked to mortality and early worsening of diabetes complications, respectively. This study will advance diabetes care by developing a novel quality measure that identifies patients in a risk-stratified way and helps clinicians tailor diabetes treatment based on a patient's unique goals of care. Such a new measure will be used by clinicians at the point-of-care and by healthcare systems for population health management.